Method of operating a telecommunications system

ABSTRACT

A method of operating a time division duplex based wireless communications system comprising the steps of establishing, at a base station, a Radio Resource Mapping Function (RMMF); deriving from the RMMF, from both the mean and standard deviation of the received signal to interference ratio (SIR) for all users, and from estimates of channel load conditions and interference levels, a Resource Metric Region (RMR) showing the number of users experiencing acceptable quality of service; and deciding, on the basis of the RMR, whether to admit a newly arriving call.

BACKGROUND TO THE INVENTION

This invention relates to a method of operating a wireless telecommunications system.

The rapidly growing demand for multi-media services over wireless communication systems has accelerated a need for more efficient utilization of the scarce and expensive wireless resource. In addition, it is important with future differentiated services to be delivered via mobile networks should be adaptable in terms of resource estimation, utilization and allocation. In next generation wireless communication systems the optimization of resource use in the radio resource management (RRM) will be a key driver. This optimization can only be made possible with the development and adoption of efficient algorithms. Current RRM functionality in 2G and 3G systems acts from the network layer down to the data link and physical layer. Thus the wireless physical resources are utilized directly without intermediate layers. Future RRM in next generation networks will be much more complex. Therefore, research work incorporating an RRM that takes account of the conditions of the physical layer resources suggests the need to focus on its potential impact in the intermediate layer, i.e. radio resource metric estimation (RME), which can be aware of the-multiple layer capabilities and states. Based on the knowledge of the desired loads and channel and radio resources, the RRM in cooperation with RME can manage both up and down the protocol stack. Thus, it can decide and control the parameters and functions required to optimize the desired features such as QoS, throughput, power utilization and overall system capacity.

Resource metric estimation (RME) is a crucial part of the radio resource allocation (RRA) algorithm that performs call admission control (CAC), resource scheduling and power/rate scheduling tasks, which provide the following control tasks:

1) The radio channel characteristics and session quality requirements are used for optimal power and rate allocation;

2) The current channel load, characteristics and quality requirements are used for controlling the resource scheduler. See L. Jorguseski et al, “Radio Resource Allocation in Third-Generation Mobile Communication Systems,” IEEE Communications Magazine, pp. 117-123, Feburary 2001.

With built-in capacity models, the RME assists the CAC in accepting or rejecting new sessions. The question of how to combine the interference measurements with the current load situation and QoS requirements of the existing traffic classes to control the CAC, or channel allocation is a very interesting issue. Thus, the most appropriate resource metrics permit efficient inter-working between the physical layer and higher layers in the protocol stack and thus it is essential to optimize the overall system performance.

The current method of assessing resource metric is to use link quality information in a long-term averaged manner. Current resource management uses this information in 2G and 3G systems and works from the network layer down to the data link and physical layer without any intermediate layers (R. Berezdivin et al., “Next-Generation Wireless Communication Concepts and Technologies”, IEEE Communication Magazine, pp. 108-116, March 2002). If the functionality of the RMMF is assumed as similar to that of the interface between the link level and the system level simulations, this has been exemplified by an interface applied to a GSM system in H. Oloffson et al, “Improved Interface between Link Level and System Level Simulation Applied to GSM,” Proceedings of IEEE ICUPC '97, pp. 79-83, August 1997 and S. Hamalainen et al, “A Novel Interface between Link and System Level Simulations,” Proceedings of ACTS Summit 1997, Aalborg, Denmark, pp. 506-604, October 1997.

The importance and functionality of RME is defined and described briefly incorporating with resource management function by Jorgueski et al (supra), and the concepts of smart resource and spectrum allocation, and utilization in next generation wireless communications are introduced and described by Hamalainen et al (supra). However, there are still problems in implementing RME in practical wireless networks.

SUMMARY OF THE INVENTION

It is an aim of the invention to provide efficient multi-layer inter-working using the resource metrics -mapping function (RMMF). It is a further aim of the invention to provide a radio resource metric region (RMR) to give an acceptable resource region where QoS and acceptable link quality can be guaranteed with an achievable resource margin to be utilized in terms of code/time/frequency resource unit.

The invention enables efficient utilizing and monitoring in resource usage using the interface between link level and system level simulations approach which allows dynamic resource metric estimation, such that the current channel load conditions and resource pool condition can be achieved in a non-averaged manner.

The present invention provides a method of operating a time division duplex based wireless communications system according to claim 1 and a base station according to claim 10. The method comprises the steps of establishing, at the base station, a Radio Resource Mapping Function (RMMF); deriving from said RMMF, from both the mean and standard deviation of the received signal to interference ratio (SIR) for all users, and from estimates of channel load conditions and interference levels, a Resource Metric Region (RMR) showing the number of users experiencing acceptable quality of service; and deciding, on the basis of the RMR, whether to admit a newly arriving call.

Preferred or optional features of the invention are defined in the dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described in more detail, by way of example only, with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram of an RMMF procedure;

FIG. 2 is a graph showing the RMMF as a function of mean and standard deviation of burst SIR;

FIG. 3 is a block diagram showing resource metric estimation;

FIG. 4 is a graph showing maximum number of users with different data rates;

FIG. 5 is a graph showing the maximum aggregated data rate for different data rate services; and

FIG. 6 is a graph of degree of confidence level.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Radio Resource Mapping Function (RMMF)

The procedure of the RMMF can be summarized by the following three stages:

-   -   1) channel characterization on a burst-by-burst basis,;     -   2) extracting burst information from link level; and     -   3) estimating link quality which can be raw BER, block error         rate, or frame error rate.

The procedure for this concept is depicted in FIG. 1, where the parameters n and m are the number of measured signal to interference ratio (SIR) in a burst and number of bursts for a specific traffic call, respectively. The SIR can be defined by SIR=G·E_(b)/I_(o) where G is the processing gain and E_(b)/I_(o) is given at Equation (2). The mapping method comprises the following steps:

-   Measure a sequence of burst SIR values and determine P^(i) _(burst)     for each burst i by means of the mapping function: raw BER=function     (burst SIR). -   Estimate the average raw BER ({circumflex over (μ)}_(BER) ^(i)) and     the average SIR ({circumflex over (μ)}_(SIR) ^(i)) for each set of     bursts and each standard deviation of the BER values ({circumflex     over (σ)}_(BER) ^(i)) and the SIR values ({circumflex over     (σ)}_(SIR) ^(i)). -   Map each parameter pair ({circumflex over (μ)}_(BER) ^(i),     {circumflex over (σ)}_(BER) ^(i)) or ({circumflex over (μ)}_(SIR)     ^(i)) on the average BER {overscore (P)}_(bi) using the link level     results and estimate link quality of each segment by using obtained     sequence of {overscore (P)}_(bi).

A computer simulation has been developed to establish the basis of the RMMF and evaluate the multirate transmission in a non-real time WWW traffic service scenario as in the ETSI Technical Report, “Selection procedures for the choice of radio transmission technologies of the UMTS,” TR 101 112 v. 3.2.0, April 1998, assuming a TDD-CDMA system model. For multirate transmission operations, multicode and multislot allocation have been considered. These can be achieved by either pooling multiple codes within one time slot or pooling of multiple time slots respectively. The simulation result shows that the average raw BER as a function of mean and standard deviation of burst SIR can be obtained as shown in FIG. 2. It should be noticed that the average raw BER is influenced not only by the mean burst quality but also the standard deviation. This 3-D figure presents the RMMF, in that it demonstrates the actual link quality according to SIR values representing channel load and standard deviation implying code availability at a current resource pool.

As described below, the availability of the current resource pool can be regarded as an available number of users that the system can support. As shown in FIGS. 4 and 5, the higher the variation of SIR, the less available the maximum capacity (and hence the smaller available resource pool).

Since the conventionally measured average SIR sample does not fully reflect the perceived dynamic link quality for users, a certain average SIR may result in very different user link quality. However, by using this kind of RMMF, the link quality in terms of average raw BER observed by a user can be determined very accurately, and furthermore accurate and fast inter-working between link level and system level can be achieved. In higher layers, this mapping function can be used as a resource look-up table having actual radio mobile channel and interference characteristics and as a monitor of resource availability or efficiency.

Radio RME with Predictive Radio Resource Metric Region (RMR) Method

We have developed a radio resource metric estimation method for TDD-based wireless communication systems. The method offers flexibility, adaptability and maximization of radio resource utilization based on predictive and acceptable radio resource metric region (RMR) in which guaranteed QoS and link quality can be assured.

In aggregated traffic, the estimation of available resource can be either optimistic or conservative due to inaccurate link quality information and the coarse estimation of overflow traffic, and thus all the resource units cannot be exploited. If we know the resource availability, i.e. the required total average resource plus the excess resource which cannot be utilized because of a stringent call admission criterion, then this information allows the acceptable resource metric region to be established on a call basis or a specific packet-length basis, thus enabling resource allocation algorithm to maximize the resource utilization.

One possible approach for this concept is depicted in FIG. 3, where the measurement of SIR and traffic followed by a Kalman based prediction is performed. This provides some measured or predicted resource parameters for the resource scheduler to search for the current position on the surface of the RMMF and to deliver the actual status of the resource usage. All of these are integrated in establishing RMR in which the calculation and estimation of resource availability are performed based on the resource region decision criteria and help the call admission decision on a call basis or a packet-length basis. This RMR should be modified and adjusted for time varying traffic channel and dynamic load conditions.

Furthermore, combined with adaptive modulation and power control, or rate control and power control, the RMR becomes a crucial decision parameter. Because of the fact that these techniques are still dependent on averaged or stringent link quality information, the resource availability becomes more uncertain and changeable and leaves a significant useful resource margin which can be exploited, even though these techniques are trying to achieve maximal resource utilization.

Conventionally, CDMA system capacity is estimated by the average received SIR (or E_(b)/I_(o)), which leads to an incorrect capacity estimation due to the neglect of the second order statistics of the SIR. In a multimedia wireless CDMA system, the maximum number of user can be achieved by the following inequality and the conditions in A. S. Sampath, P. 5. Kumar, and J. M. Holtzman, “Power control and resource management for a multimedia CDMA wireless system,” in Proc. IEEE PIMRC '95, 1995, assuming the existence of the optimal power level for each user. $\begin{matrix} {{{\sum\limits_{j = 1}^{K}\quad\frac{ɛ_{j}}{ɛ_{j} + G_{j}}} + \frac{\eta_{0}W}{\min\limits_{l \leq i \leq K}\left\lbrack {P_{\max,i}h_{i}\frac{ɛ_{i} + G_{i}}{ɛ_{i}}} \right\rbrack^{20}}} < 1} & (1) \end{matrix}$ where W is the system bandwidth, G_(j) is the processing gain of the user, the channel coefficient h_(j) accounts for the combined effects of path loss and shadowing, and n₀ is the noise spectral density. K is the total number of mobile users admitted to the system, which is the total of each user's supporting corresponding data rate classes K=Σ_(i=i) ^(N)K_(i), where N is the total data rate of all classes of services. ε_(j) is the required E_(b)/I_(o) for the i-th user, which is defined by $\begin{matrix} {ɛ_{i} = \frac{G_{i}h_{i}P_{i}}{{\sum\limits_{j \neq i}^{K}\quad{h_{j}P_{j}}} + {\eta_{0}W}}} & (2) \end{matrix}$

The maximum number of supportable users K is determined by the predefined outage probability, which is set in this example to 0.05 and is defined by $\begin{matrix} {P_{out} = {{\Pr\left( {{{\sum\limits_{j = 1}^{K}\quad\frac{ɛ_{j}}{ɛ_{j} + G_{j}}} + \frac{\eta_{0}W}{\min\limits_{l \leq i \leq K}\left\lbrack {P_{\max,i}h_{i}\frac{ɛ_{i} + G_{i}}{ɛ_{i}}} \right\rbrack}} \geq 1} \right\}} = 0.05}} & (3) \end{matrix}$ where each terminal i is power-limited by P_(max,i), then $\begin{matrix} {{0 < P_{i}} = {\frac{\eta_{0}W\frac{ɛ_{i}}{ɛ_{i} + G_{i}}}{h_{i}\left( {1 - {\sum\limits_{j = 1}^{K}\quad\frac{ɛ_{j}}{ɛ_{j} + G_{j}}}} \right\}} \leq {P_{\max,i}{\forall i}}}} & (4) \end{matrix}$

As shown in FIG. 4, the theoretical maximum available number of user pair (K₁, K₂) with different data rates and QoS requirements (or the predefined. E_(b)/I_(o) requirement) is significantly dependent on the variance of E_(b)/I_(o), caused by systematic error such as inaccurate power control, traffic variation and fading. In FIG. 4, we assume a single cell and uplink model having spreading bandwidth, W=3.84 MHz, supporting two data rate services (R₁, R₂)=(60,240) Kbps with different required E_(b)/I_(o), i.e. 5.0 dB and 5.5 dB, respectively, and the maximum allowable powers (P_(max1) , P_(max2)) to each two data group users is set to 5 dBm and 20 dBm. The noise spectral density is assumed as −174 dBm/Hz.

If the systematic error is increased, then the maximum available number of users in a system is dramatically reduced as shown in FIGS. 4 and 5. Thus, it is necessary to consider the variance of the received E_(b)/I_(o) (or SIR, because this is easily converted to SIR) along with the possible maximum capacity margin according to the situation. Note that due to the higher variance of the E_(b)/I_(o) the maximum capacity decreases, as do the capacity margin and resource availability. For an estimation of the maximum available number of users and available resource margin, both the received E_(b)/I_(o) and the variance of E_(b)/I_(o) have to be taken into account.

Resource Metric Estimation and Admission with RMMF

In order to explain the practical procedure of resource metric estimation and admission with RMMF (in FIGS. 1 and 2) and the RMR concept (in FIG. 3), let us begin at point {circle around (A)}, {circle around (B)} and {circle around (C)} as shown in FIG. 4.

This procedure can be summarized as follows:

-   -   1) From the RMMF procedure as explained above (in FIG. 1), the         base station (BS) measures the received SIR (or E_(b)/I_(o)) and         delivers the mean value and standard deviation of received SIR.     -   2) Based on this information, the BS can estimate the current         channel load condition and interference level. Then, the users         satisfying the E_(b)/I_(o) requirement can be selected from the         aggregated received signal.     -   3) When the number of users satisfying QoS requirement is         determined based on 2), this should be a set of three capacity         regions: {circle around (A)}, feasible region; {circle around         (B)}, negotiable region; {circle around (C)}, non-feasible         region.     -   4) From the analytical expression, Equation (1), the system         knows the maximum available number of users according to the         variation of E_(b)/I_(o). For example, in FIG. 4 the maximum         upper limit is MUL and the maximum lower limit is MLL if the         maximum possible standard deviation of E_(b)/I_(o) is assumed to         be 4.0 dB. The MUL is the optimistic capacity limit and the MLL         is the too stringent capacity limit at the conventional call         admission control algorithm. The call admission control         algorithm based on this limit has a hard capacity limit or a         hard admission decision criterion. These two theoretical         capacity limits can be calculated by Equation (1).

Let the left side of Equation (1) be rewritten as f(ε, h, G)<1→(K _(MUL) , K _(MLL))   (5) where ε[ε₁, . . . , ε_(K)] is the E_(b)/I_(o) vector, h=[h₁, . . . , h_(K)] is the channel coefficient vector, and G=[G₁, . . . , G_(K)] is the processing gain vector. K_(MUL) is the maximum upper limit and K_(MLL) is the maximum lower limit. As a result of the procedure in FIG. 3, we can obtain the measured E_(b)/I_(o), {tilde over (ε)}_(i), at the measurement block. Based on this value and step 1), we can measure and estimate the available number of users {tilde over (K)} in the current system state. Depending on Equation (1), the newly arriving call is accepted or rejected.

If f(ε, h, G)<1, then this system state is accepted, i.e. all the users in the current state are being served. Otherwise, some users' calls can be rejected. However, this is a quite conventional decision rule.

Therefore, herein, we introduce a modified rule, which is dependent on the standard deviation of capacity and the degree of confidence of capacity that can be derived from the standard deviation of E_(b)/I_(o). First, we define the difference of capacity limits, ΔK_(MUL)=K_(MUL)−{tilde over (K)} and ΔK_(MLL)=K_(MLL)−{tilde over (K)}. After Kalman prediction in FIG. 3, we can obtain the predicted E_(b)/I_(o) vector, {circumflex over (ε)}=[{circumflex over (ε)}₁, . . . , {circumflex over (ε)}_(K)], the standard deviation of the E_(b)/I_(o) vector, {circumflex over (ε)}_(σ)=[{circumflex over (ε)}_(σ,1), . . . , {circumflex over (ε)}_(σ,K)], and can predict the available number of users {tilde over (K)} with a variance Δ{tilde over (K)}, which can be written as {tilde over (K)}={tilde over (K)}±Δ{tilde over (K)}={tilde over (K)}±Δf({circumflex over (ε)}, {circumflex over (ε)}_(σ) , ĥ, {circumflex over (G+EE, c) (6) )} where Δf({circumflex over (ε)}, {circumflex over (ε)}_(σ), ĥ, {circumflex over (ε)}, {circumflex over (ε)}_(σ), ĥ, Ĝ, and the coefficient derived from the normalized Gaussian distribution, c. Thus, Δ{tilde over (K)} is the variance of the available number of users either increased or decreased by the Kalman prediction, which is subject to the time-varying traffic variation caused by arriving or departing users. This can be calculated straightforwardly from the capacity estimation equation, that is $\begin{matrix} {{\Delta\overset{\sim}{K}} = {\overset{\sim}{K}\left( \frac{\left\langle {{c \cdot E}\left\{ {\overset{\sim}{ɛ}}_{\sigma} \right\}} \right\rangle}{\left\langle {{c \cdot E}\left\{ {\overset{\sim}{ɛ}}_{\sigma} \right\}} \right\rangle + 1} \right)}} & (7) \end{matrix}$ where <·>=10^((·)/10) and E{·} is the expectation value. If the multirate services are taken into account in this equation, each expectation value of the standard deviation corresponding to the respective data rate service should be calculated and the estimated number of users for that data rate should be summed.

Next, we introduce the degree of confidence level (DCL) of users according to the above description. The DCL can be defined as the capacity status by deciding the safe distance from the optimistic capacity limit (i.e. MUL) and the pessimistic capacity limit (i.e. MLL). This can be defined as follows: ${{IF}\quad\overset{\sim}{K}} \in {(A)\quad{AND}\quad\hat{K}} \in (A)$ $\begin{matrix} {{{DCL} = 1}{{{ELSEIF}\quad\overset{\sim}{K}} \in {(A)\quad{AND}\quad\hat{K}} \notin (A)}{{{IF}\quad\left( {{\Delta\overset{\sim}{K}} \geq {\Delta\quad K_{MLL}}} \right)\quad{and}\quad\left( {{\Delta\quad\overset{\sim}{K}} < {\Delta\quad K_{MUL}}} \right)},}} & \\ {{DCL} = {1 - {\alpha\left( \frac{\Delta\overset{\sim}{K}}{\Delta\quad K_{MUL}} \right)} + C}} &  \end{matrix}$ where a(S) is a monotonously increasing function decided by empirical approach, which can be a log-sigmoid function or a hyperbolic tangent function. C is the constant. ELSEIF(Δ{tilde over (K)}≧ΔK_(MLL)) and (Δ{tilde over (K)}≧ΔK_(MUL)), DCL=0   {circle around (3)} (this call is queued and awaiting the next stage with high priority.) END ELSEIF {tilde over (K)} ε (B) AND {tilde over (K)} ε (A) IF (Δ{tilde over (K)}≧ΔK_(MLL)) and (Δ{tilde over (K)}<ΔK_(MUL)), $\begin{matrix} {{DCL} = {1 - \frac{{\beta \cdot \Delta}\quad\overset{\sim}{K}}{\Delta\quad K_{MUL}}}} &  \end{matrix}$ where β is a positive coefficient, 0<β≦½. $\begin{matrix} {{DCL} = {1 - \frac{{\gamma \cdot \Delta}\quad\overset{\sim}{K}}{\Delta\quad K_{MUL}}}} &  \end{matrix}$ where γ is a positive coefficient, ½<γ≦1. END ELSEIF {tilde over (K)} ε (B) AND {circumflex over (K)} ε (B) $\begin{matrix} {{DCL} = {1 - \frac{\left( {{\Delta\quad K_{MLL}} - {\Delta\quad\overset{\sim}{K}}} \right)}{\left( {{\Delta\quad K_{MUL}} - {\Delta\quad K_{MLL}}} \right)}}} &  \end{matrix}$ ELSEIF {tilde over (K)} ε ( B) AND {circumflex over (K)} ε (C) DCL=0   {circle around (7)} ELSEIF {tilde over (K)} ε (C) AND {circumflex over (K)} ε (B) $\begin{matrix} {{DCL} = \frac{\Delta\quad\overset{\sim}{K}}{\left( {{\Delta\quad K_{MUL}} - {\Delta\quad K_{MLL}}} \right)}} &  \end{matrix}$ ELSEIF {tilde over (K)} ε (C) AND {circumflex over (K)} ε (C) DCL=0 END   {circle around (9)}

-   -   5) At the region {circle around (A)}, i.e. inside the feasible         capacity region, all the detected users can be supported in the         system since their QoS are guaranteed. At the region {circle         around (C)} outside the maximum available capacity limit, i.e.         in the non-feasible region, almost all the users in this region         should await their turn. The region {circle around (B)}, between         the feasible and the non-feasible region, indicates that even         though there are some users satisfying the QoS requirement, the         system cannot accept these users because it cannot provide a         reliable traffic channel to them.

Consequently, this DCL and predicted capacity can be converted into the resource unit, since all the possible resource units such as code unit, time slot unit and frequency unit can be directly related to the data rate and its corresponding processing gain. Thus, the DCL procedure as described above performs adaptive call admission control, taking the capacity and resource availability in an adaptive and predictive manner. This DCL can be depicted as shown in FIG. 6 where the degree of confidence is heavily dependent on the Δ{tilde over (K)}, that is the variation of QoS requirement. Thus, this method has a functionality of adaptive QoS based admission control.

Moreover, for next generation wireless communication systems, the resource unit can be enlarged to frequency unit (multi-carrier system), spatial unit (multiple-input-multiple-output system) and virtual path unit (Ad-Hoc network). In such evolved systems, this concept performs the intermediate layer function so that high layers could obtain actual and adaptive information concerning the resource status of the link layer, and thus enable adaptively flexible radio resource allocation.

Whilst the invention may be dependent on the load behavior, traffic assumption and selected scenarios, the important point is that the invention enables radio resource management to be dynamic and adaptable to multimedia services in next generation wireless systems.

All forms of the verb “to comprise” used in this specification have the meaning “to consist of or include”. 

1. A method of operating a time division duplex based wireless communications system comprising the steps of establishing, at a base station, a Resource Metric Mapping Function (RMMF); deriving from said RMMF, from both the mean value and standard deviation of the received signal to interference ratio (SIR) for all users, and from estimates of channel load conditions and interference levels, a Resource Metric Region (RMR) showing the number of users experiencing acceptable quality of service; and deciding, on the basis of the RMR, whether to admit a newly arriving call.
 2. A method according to claim 1, wherein the step of deriving the RMR comprises Kalman prediction of an interference vector comprising a predicted interference value for each user.
 3. A method according to claim 2, wherein the interference vector and the standard deviation thereof are used to predict an available number of users.
 4. A method according to claim 1, wherein the RMR shows whether the experiencing acceptable quality of service is above or below a maximum upper limit and also whether said number is above or below a maximum lower unit.
 5. A method according to claim 4, comprising establishing a degree of confidence level for users as a function of the distance of the total number of users from said maximum upper limit and of the distance of the total number of users from said maximum lower limit, and wherein the step of deciding whether to admit a newly arriving call comprises taking into account said degree of confidence level.
 6. A method according to claim 1, wherein the RMMF is established on the basis of the mean and standard deviation of both the bit error rate (BER) and the SIR.
 7. A method according to claim 6, wherein SIR values are measured as a sequence of burst values.
 8. A method according to claim 7, wherein a BER value is determined for each SIR burst value as a function thereof.
 9. A method according to claim 7, wherein link quality is estimated by mapping pairs of parameters, each pair comprising the mean and standard deviation of BER or SIR, onto the average BER.
 10. A base station for use in a time division duplex based wireless communications system, the base station comprising means for establishing a Resource Metric Mapping Function (RMMF); means for deriving from said RMMF, from both the mean value and standard deviation of the received signal to interference ratio (SIR) for all users, and from estimates of channel load conditions and interference levels, a Resource Metric Region (RMR) showing the number of users experiencing acceptable quality of service; and means for deciding, on the basis of the RMR, whether to admit a newly arriving call.
 11. A base station according to claim 10, wherein the means for deriving the RMR comprises means for Kalman prediction of an interference vector comprising a predicted interference value for each user.
 12. A base station according to claim 11, wherein the RMR shows whether the experiencing acceptable quality of service is above or below a maximum upper limit and also whether said number is above or below a maximum lower limit and the base station comprises means for establishing a degree of confidence level for users as a function of the distance of the total number of users from said maximum upper limit and of the distance of the total number of users from said maximum lower limit, and wherein the means for deciding whether to admit a newly arriving call comprises means taking into account said degree of confidence level. 